Synergic Antibacterial Effect of Curcumin with Ampicillin; Free Drug Solutions in Comparison with SLN Dispersions
Bibliographic record
Abstract
Purpose: This study was designed to investigate benefit of using nanotechnology on increasing of synergic antibacterial effect of natural and chemical antibacterial agents. Methods: At first the MIC and MBC of Curcumin and Ampicillin as selected antibacterial agents was determined, after that Solid Lipid Nanoparticles (SLNs) of each active ingredients as well as Curcumin-Ampicillin loaded SLNs were prepared using high pressure homogenization technique. Characterization of prepared SLNs was done, then MIC, MBC and contact killing time were investigated for Curcumin-Ampicillin loaded SLNs in comparison with free Curcumin and Ampicillin solutions as well as Ampicillin and Curcumin SLNs. Results: Based on results nanoparticles with the size of 150 nm show much more decreased MIC and MBC when Ampicillin and Curcumin were loaded together on SLNs than solutions in which free Ampicillin and Curcumin were mixed. Conclusion: It seems that using nanotechnology could cause decrease the dosage of antibiotics and risk of having antibiotic resistance bacteria strains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".